{"title":"海鸟型溶解氧光电传感器漂移特性研究","authors":"Alice S. Ren, Daniel L. Rudnick, Alistair Twombly","doi":"10.1175/jtech-d-22-0103.1","DOIUrl":null,"url":null,"abstract":"Abstract The Sea-Bird 63 dissolved oxygen optode sensors used on various oceanographic platforms are known to drift over time. Corrections for drift are necessary for accurate dissolved oxygen measurements on the timescale of months to years. Here, drift on 14 Sea-Bird 63 dissolved oxygen optode sensors deployed on Spray underwater gliders over 5 years is described. The gliders with oxygen sensors were deployed regularly for 100-day missions as part of the California Underwater Glider Network (CUGN). A laboratory two-point calibration was performed on the oxygen sensor before and after glider deployment. Sensor drift during 100-day deployments was larger than during 100-day storage periods. Sensor behavior is modeled with a gain that asymptotes to 1.090 ± 0.005 with an e-folding timescale of 3.70 ± 0.361 years. At zero oxygen concentration, the sensor consistently reads around 3 μmol kg −1 ; a negative offset term is used in addition to the gain to correct the sensor oxygen. The correction procedure removes the error due to long time drift, one of the major sources of error, with an uncertainty of 0.5% (0.9% including outliers) or 0.5 μmol kg −1 depending on concentration, which improves the accuracy of the Sea-Bird 63 although uncertainty from other sources of error including the initial factory calibration and the sensor response time remain. Suggested procedures for implementing a two-point calibration procedure in the laboratory are discussed. Calibrations must be considered starting 6 months after initial factory calibration to keep error from sensor time drift under 1%.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":"18 1","pages":"0"},"PeriodicalIF":1.9000,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Drift characteristics of Sea-Bird dissolved oxygen optode sensors\",\"authors\":\"Alice S. Ren, Daniel L. Rudnick, Alistair Twombly\",\"doi\":\"10.1175/jtech-d-22-0103.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract The Sea-Bird 63 dissolved oxygen optode sensors used on various oceanographic platforms are known to drift over time. Corrections for drift are necessary for accurate dissolved oxygen measurements on the timescale of months to years. Here, drift on 14 Sea-Bird 63 dissolved oxygen optode sensors deployed on Spray underwater gliders over 5 years is described. The gliders with oxygen sensors were deployed regularly for 100-day missions as part of the California Underwater Glider Network (CUGN). A laboratory two-point calibration was performed on the oxygen sensor before and after glider deployment. Sensor drift during 100-day deployments was larger than during 100-day storage periods. Sensor behavior is modeled with a gain that asymptotes to 1.090 ± 0.005 with an e-folding timescale of 3.70 ± 0.361 years. At zero oxygen concentration, the sensor consistently reads around 3 μmol kg −1 ; a negative offset term is used in addition to the gain to correct the sensor oxygen. The correction procedure removes the error due to long time drift, one of the major sources of error, with an uncertainty of 0.5% (0.9% including outliers) or 0.5 μmol kg −1 depending on concentration, which improves the accuracy of the Sea-Bird 63 although uncertainty from other sources of error including the initial factory calibration and the sensor response time remain. Suggested procedures for implementing a two-point calibration procedure in the laboratory are discussed. 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引用次数: 0
摘要
摘要海鸟63型溶解氧光电传感器用于各种海洋学平台,已知会随时间漂移。为了在月到年的时间尺度上精确地测量溶解氧,对漂移进行校正是必要的。本文描述了5年来部署在Spray水下滑翔机上的14个海鸟63溶解氧光电传感器的漂移情况。这些装有氧气传感器的滑翔机作为加州水下滑翔机网络(CUGN)的一部分,定期执行为期100天的任务。在滑翔机展开前后对氧传感器进行了实验室两点标定。在100天的部署期间,传感器漂移大于100天的存储期间。传感器行为模型的增益渐近线为1.090±0.005,电子折叠时间标度为3.70±0.361年。在零氧浓度下,传感器读数稳定在3 μmol kg−1左右;除增益外,还使用负偏移项来校正传感器氧。校正过程消除了由于长时间漂移引起的误差,这是误差的主要来源之一,根据浓度的不同,不确定性为0.5%(包括异常值为0.9%)或0.5 μmol kg - 1,这提高了海鸟63的精度,尽管其他误差来源(包括初始工厂校准和传感器响应时间)的不确定性仍然存在。讨论了在实验室实施两点校准程序的建议程序。必须考虑在初始工厂校准后6个月开始校准,以使传感器时间漂移的误差低于1%。
Drift characteristics of Sea-Bird dissolved oxygen optode sensors
Abstract The Sea-Bird 63 dissolved oxygen optode sensors used on various oceanographic platforms are known to drift over time. Corrections for drift are necessary for accurate dissolved oxygen measurements on the timescale of months to years. Here, drift on 14 Sea-Bird 63 dissolved oxygen optode sensors deployed on Spray underwater gliders over 5 years is described. The gliders with oxygen sensors were deployed regularly for 100-day missions as part of the California Underwater Glider Network (CUGN). A laboratory two-point calibration was performed on the oxygen sensor before and after glider deployment. Sensor drift during 100-day deployments was larger than during 100-day storage periods. Sensor behavior is modeled with a gain that asymptotes to 1.090 ± 0.005 with an e-folding timescale of 3.70 ± 0.361 years. At zero oxygen concentration, the sensor consistently reads around 3 μmol kg −1 ; a negative offset term is used in addition to the gain to correct the sensor oxygen. The correction procedure removes the error due to long time drift, one of the major sources of error, with an uncertainty of 0.5% (0.9% including outliers) or 0.5 μmol kg −1 depending on concentration, which improves the accuracy of the Sea-Bird 63 although uncertainty from other sources of error including the initial factory calibration and the sensor response time remain. Suggested procedures for implementing a two-point calibration procedure in the laboratory are discussed. Calibrations must be considered starting 6 months after initial factory calibration to keep error from sensor time drift under 1%.
期刊介绍:
The Journal of Atmospheric and Oceanic Technology (JTECH) publishes research describing instrumentation and methods used in atmospheric and oceanic research, including remote sensing instruments; measurements, validation, and data analysis techniques from satellites, aircraft, balloons, and surface-based platforms; in situ instruments, measurements, and methods for data acquisition, analysis, and interpretation and assimilation in numerical models; and information systems and algorithms.